Biomedical Named Entity Linking (BioNEL) is the task of mapping mentions extracted from biomedical texts to canonical entities or concepts in the standardized knowledge base. Although existing methods have achieved remarkable performance in mapping accuracy, most rely on training with domain-specific datasets, which fu...
Ziyi Chen, Hui Liu, Ziyang Ling et al.· 0 citations
Bengali has no grammatical gender, but honorificity is obligatory: every third-person reference must choose ordinary or respectful register. English has no such signal, so the system must choose a level of deference that the source never specified. We use this property to measure how large language models (LLMs) alloca...
Ajwad Abrar, Md. Kamrul Hasan, Hasan Mahmud· 0 citations
Nheengatu (yrl), the Amazonian L\'ingua Geral, has 6--10 thousand speakers and is co-official in S\~ao Gabriel da Cachoeira (Brazil), yet remains poorly served by evaluated Portuguese--Nheengatu MT resources. We present the largest parallel corpus assembled for the language (14.5k Portuguese--Nheengatu pairs), anchored...
Fabr\'icio Carraro, Rodolfo Joel Zevallos, Rodrigo Gon\c{c}alves de Souza et al.· 0 citations
The predominance of English-language Web content creates an information-access barrier for speakers of low-resource languages such as Amharic. This paper presents the design, imple-mentation, and evaluation of a bidirectional Amharic-English cross-language question answering (CLQA) system that allows a user to formulat...
Emebet Bekele, Fekade Getahun· 0 citations
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Large Vision-Language Models (LVLMs) have achieved remarkable progress across a wide range of multimodal tasks. However, object hallucination remains a persistent challenge, undermining model reliability in applications such as visual question answering, embodied agents, and human-AI decision support systems. In this p...
Xinhao Wang, Junbo Cui, Zhenghao Liu et al.· 0 citations
To enable large language models to generate outputs that align with human preferences, optimization algorithms based on the comparison between chosen and rejected output pairs have been developed and been proven effective. Some recent endeavors have utilized certain linguistically-derived assumptions about the preferen...
The first benchmark tailored for long historical document understanding is introduced, and various modal retrievers and the end-to-end RAG performance to provide references for Retrieval-Augmented Generation (RAG) systems are assessed.
Zeyu Shan, Yong-Xin Shi, Shi-Hang Wu et al.· 0 citations
Deploying language models (LMs) on the edge presents unique memory, latency, and power challenges absent in high-performance computing. KV-cache compression emerges as a promising approach to address these challenges. However, the effectiveness of current KV-cache compression methods has not been thoroughly examined on...
S. M. Mahbub Murshed, Hayden Bright, Rezaul Karim et al.· 0 citations
In Chinese content governance, large language models are increasingly used to identify toxic and discriminatory expressions, yet both task operationalization and ironic expression can alter bias measurement readings. We conducted a preregistered multi-task behavioral audit of multiple Chinese large language models. In...
A challenging task in which an LLM predicts the relation between two glosses (definitions of senses) presented in two different orders, suggesting limitations in current LLMs's semantic relation knowledge, both in identifying relations and in respecting asymmetry.
Zhi-Han Cao, Adam Nohejl, Hitomi Yanaka· 0 citations
Do Large Language Models (LLMs) genuinely grasp the truth conditions of plural descriptions, or do they force every sentence into true or false? We investigate the Homogeneity Gap, a semantic phenomenon where a plural definite (e.g., the kids laughed) and its negation (the kids didn't laugh) are both neither true nor f...
Duaa Nakshbandi, Zhiyu An, Yiping Li et al.· 0 citations
Scientific research increasingly depends on curating experimental data from literature. Scientific data curation requires verifying exact experimental conditions (e.g., specific assays, clinical outcomes, and data availability) across long, heterogeneous files, which is a highly time-consuming process. Existing benchma...
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Microsoft Research Blog· microsoft.comOct 15, 2026
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
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